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This study addresses the challenges faced by highways and expressways, emphasizing the crucial role of V2N communication and machine learning within modern transportation systems, especially in the context of IoT networks. The research underscores Q-learning’s superior resource allocation capabilities compared to random forest, particularly in high-demand scenarios. These findings illuminate the potential of advanced technologies, coupled with IoT networks, to alleviate traffic congestion and enhance safety in highway road networks. The study emphasizes the significance of dynamic resource allocation using machine learning to optimize vehicular network performance and reliability.

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Performance Analysis of Linear V2N Networks Resource Allocation Employing Q-Learning and Random Forest ML Algorithms

  • 会议论文

  • 发表日期：

    2025年

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  • Divyanshu Pandey,
  • K. L. V. Sai Prakash Sakuru

摘要

This study addresses the challenges faced by highways and expressways, emphasizing the crucial role of V2N communication and machine learning within modern transportation systems, especially in the context of IoT networks. The research underscores Q-learning’s superior resource allocation capabilities compared to random forest, particularly in high-demand scenarios. These findings illuminate the potential of advanced technologies, coupled with IoT networks, to alleviate traffic congestion and enhance safety in highway road networks. The study emphasizes the significance of dynamic resource allocation using machine learning to optimize vehicular network performance and reliability.